neuralib.atlas.brainrender.roi.RoiRenderCLI

class neuralib.atlas.brainrender.roi.RoiRenderCLI

Bases: BrainRenderCLI

ROIs reconstruction with brainrender

__init__()

Methods

__init__()
export(reconstructor[, output, areas, alpha])	Export reconstruction as html
get_atlas_brain_globe([check_latest])
main([args, parse_only, system_exit])	parsing the commandline input args and call run().
new_parser(**kwargs)	create an ArgumentParser.
post_parsing()
render()	brainrender interactive
render_output()	io handling.
run()	called after main()
video_maker(output_file)	generate video

Attributes

DEFAULT_REGION_COLORS
DEFAULT_ROI_COLORS
DESCRIPTION	parser description.
EPILOG	parser epilog.
GROUP_OPTIONAL
GROUP_OUTPUT
GROUP_REGION
GROUP_ROIS
GROUP_ROIS_LOAD
GROUP_SETTINGS
USAGE	parser usage.
annotation	whether draw point annotation.
background	background color
camera_angle	camera angle
classifier_file	csv output file from allenccf
coordinate_space	which coordinate space, by default ccf
file	points file as 'npy' or 'csv'
hemisphere	which hemisphere for rendering the region
inverse_lut	inverse right/left maps to ipsi/contra hemisphere look up table
logger
no_root	render without root(brain) mesh
output	output path for the html, if None, preview
print_name	print acronym and the corresponding name
print_tree	print tree for the available regions for the given source
radius	each roi radius
region_col	if None, auto infer, and check the lowest merge level contain all the regions specified
region_colors	region(s) color
regions	region(s) name
regions_alpha	region alpha value
roi_alpha	region alpha value
roi_colors	colors of rois per region
roi_region	only show rois in region(s)
root_alpha	root alpha
shader_style	Shader style to use
source	atlas source name.
source_order	source order to follow the roi_colors
title	title added to the top of the window
tree_init	init region for the tree print
video_output	video output path
scene

DESCRIPTION: str = 'ROIs reconstruction with brainrender'

parser description. Could be override as a method if its content is dynamic-generated.

DEFAULT_ROI_COLORS = ['orange', 'magenta', 'dimgray']

GROUP_ROIS = 'ROI View Option'

roi_region: str | tuple[str, ...]

only show rois in region(s)

radius: float

each roi radius

roi_alpha: float

region alpha value

roi_colors: str | tuple[str, ...]

colors of rois per region

region_col: str | None

if None, auto infer, and check the lowest merge level contain all the regions specified

inverse_lut: bool

inverse right/left maps to ipsi/contra hemisphere look up table

source_order: tuple[str, ...] | None

source order to follow the roi_colors

GROUP_ROIS_LOAD = 'ROI load Option'

classifier_file: Path | None

csv output file from allenccf

file: list[Path] | None

points file as ‘npy’ or ‘csv’

run()

called after main()

render()

brainrender interactive